Unless you’ve been living under a rock, you know that the COVID-19 pandemic has rocked nearly every industry around the world in a variety of ways. Most notably, the once-predictable global supply chain has experienced unprecedented shortages and delays. And unlike previous disruptions due to geographically-limited crises, the global nature of this event means experts say we’re looking at a years-long bottleneck.
Complex supply chain challenges are here to stay–at least for the foreseeable future–and your business has to find a way to keep thriving in spite of them. Supply chain leaders have access to artificial intelligence, machine learning, natural language processing all wrapped up in a technological advancement–now more than ever, implementing cognitive automation (or intelligent automation) is the way to disaster-proof your supply chain.
Container shipping has been the industry standard for years, with rates and availability holding relatively steady with inflation until COVID-19 hit. Now–thanks to everything from closures of container manufacturing facilities for months at a time to congested ports with ships waiting days or weeks to offload–this preferred method of moving large volumes of inventory around is now at a minimum about four times more expensive than it was pre-pandemic.
Can your business afford that price increase? Maybe, but if you could optimize your supply chain to use less expensive options, that would be ideal. How many data analysts and how much demand planning would you need to do that, do you suppose?
No matter how many experts you hire, spoiler alert: they can never approach the prediction accuracy of cognitive systems using a harmonized data layer. In this case, cost-management in shipping might mean monitoring and predicting container costs, port capacities, delivery workforce, timetables of production, and even more logistics. Without cognitive computing, your supply chain manager is a single person being asked to track way too much data.
When all relevant factors are fed into a dynamic learning system, touchless planning not only becomes possible, it exceeds human ability to predict and manage cost variability. This not only allows for targeted, strategic spending, it frees up the humans in the organization to do things like come up with new shipping alternatives or plan for in-house manufacture of previously imported components.
It’s a bit of a chicken-and-the-egg situation, but the pandemic has caused shortages in everything from crude materials to automobiles and everything in between. Obviously a shortage in any given building block begets a shortage in the products made from it, but the reasons for specific shortages vary and can be complex.
Regardless of the driving factors, if your business needs something you can’t get, the results are the same: disruption, inventory shortages, falling profits, and (quite likely) panic.
In a business world dominated by scarcity, time is of the essence. Making purchasing decisions based on last month’s reports might’ve worked a decade ago, but in this age of disruption, it’s about as useful as throwing a dart at the wall to guide your decision.
The only way to succeed in spite of shortages is to 1) have the most data possible and 2) have the best analysis of that data at the ready. The recent chip shortage in the US is a perfect example of how cognitive technology can process all available data (the keyword there is available) and drive adaptable decision-making based on that data.
And again, it’s probably no surprise that such a system not only capitalizes on what is available, it allows the human workforce to concentrate on creative solutions to work around those shortages.
It is perhaps unfair but true: As supply chains experience more and more disruption, end consumers are expecting more and more immediacy. From the rise in e-commerce during the pandemic to expectations of immediate customer service, today’s customers want services and goods as quickly as possible.
And while inventory optimization was hard enough when the supply chains were humming along, can companies even survive through multiple levels of disruption?
They can, but they need help.
“We don’t know when that will be available” is a death knell in the customer service realm. Ideally, whatever is wanted is available; the next best thing is being able to clarify when it will be available.
Cognitive automation not only optimizes decision making throughout the product life cycle, it enables more precise, on-demand forecasting–useful at any time, of course, but imperative when supply chains are disrupted and consumers still want speedy service.
Supply chain challenges aren’t ending any time soon, and the only way for businesses to survive these disruptions is to change their business operations and implement agile, continuously-learning systems of data analysis and decision making. Cognitive automation is currently the only lifeboat in the supply chain disruption ocean.